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Rustandi T, Yumassik AM, Ilahi FS, Alfian R, Prihandiwati E, Susanto Y, Susilo YH, Ulfah M, Faizatun F. Repurposing therapy of ibrexafungerp vulvovaginal candidiasis drugs as cancer therapeutics. Front Pharmacol 2024; 15:1428755. [PMID: 38994207 PMCID: PMC11236599 DOI: 10.3389/fphar.2024.1428755] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/07/2024] [Accepted: 05/31/2024] [Indexed: 07/13/2024] Open
Affiliation(s)
- Tedi Rustandi
- Pharmacy, Sekolah Tinggi Ilmu Kesehatan ISFI Banjarmasin, Banjarmasin, Indonesia
| | | | - Fitrah Shafran Ilahi
- Pharmacy, Sekolah Tinggi Ilmu Kesehatan ISFI Banjarmasin, Banjarmasin, Indonesia
| | - Riza Alfian
- Pharmacy, Sekolah Tinggi Ilmu Kesehatan ISFI Banjarmasin, Banjarmasin, Indonesia
| | - Erna Prihandiwati
- Pharmacy, Sekolah Tinggi Ilmu Kesehatan ISFI Banjarmasin, Banjarmasin, Indonesia
| | - Yugo Susanto
- Pharmacy, Sekolah Tinggi Ilmu Kesehatan ISFI Banjarmasin, Banjarmasin, Indonesia
| | - Yudi Hardi Susilo
- Pharmacy, Sekolah Tinggi Ilmu Kesehatan ISFI Banjarmasin, Banjarmasin, Indonesia
| | - Maria Ulfah
- Pharmacy, Sekolah Tinggi Ilmu Kesehatan ISFI Banjarmasin, Banjarmasin, Indonesia
| | - Faizatun Faizatun
- Department of Pharmaceutical Technology, Faculty of Pharmacy, Pancasila University, Jakarta, Indonesia
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Odongo R, Demiroglu-Zergeroglu A, Çakır T. A network-based drug prioritization and combination analysis for the MEK5/ERK5 pathway in breast cancer. BioData Min 2024; 17:5. [PMID: 38378612 PMCID: PMC10880212 DOI: 10.1186/s13040-024-00357-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/16/2023] [Accepted: 02/12/2024] [Indexed: 02/22/2024] Open
Abstract
BACKGROUND Prioritizing candidate drugs based on genome-wide expression data is an emerging approach in systems pharmacology due to its holistic perspective for preclinical drug evaluation. In the current study, a network-based approach was proposed and applied to prioritize plant polyphenols and identify potential drug combinations in breast cancer. We focused on MEK5/ERK5 signalling pathway genes, a recently identified potential drug target in cancer with roles spanning major carcinogenesis processes. RESULTS By constructing and identifying perturbed protein-protein interaction networks for luminal A breast cancer, plant polyphenols and drugs from transcriptome data, we first demonstrated their systemic effects on the MEK5/ERK5 signalling pathway. Subsequently, we applied a pathway-specific network pharmacology pipeline to prioritize plant polyphenols and potential drug combinations for use in breast cancer. Our analysis prioritized genistein among plant polyphenols. Drug combination simulations predicted several FDA-approved drugs in breast cancer with well-established pharmacology as candidates for target network synergistic combination with genistein. This study also highlights the concept of target network enhancer drugs, with drugs previously not well characterised in breast cancer being prioritized for use in the MEK5/ERK5 pathway in breast cancer. CONCLUSION This study proposes a computational framework for drug prioritization and combination with the MEK5/ERK5 signaling pathway in breast cancer. The method is flexible and provides the scientific community with a robust method that can be applied to other complex diseases.
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Affiliation(s)
- Regan Odongo
- Department of Bioengineering, Faculty of Engineering, Gebze Technical University, Gebze, Kocaeli, 41400, Turkey.
| | - Asuman Demiroglu-Zergeroglu
- Department of Molecular Biology & Genetics, Faculty of Science, Gebze Technical University, Gebze, Kocaeli, 41400, Turkey
| | - Tunahan Çakır
- Department of Bioengineering, Faculty of Engineering, Gebze Technical University, Gebze, Kocaeli, 41400, Turkey
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Zhou H, Liu H, Yu Y, Yuan X, Xiao L. Informatics on Drug Repurposing for Breast Cancer. Drug Des Devel Ther 2023; 17:1933-1943. [PMID: 37405253 PMCID: PMC10315146 DOI: 10.2147/dddt.s417563] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/17/2023] [Accepted: 06/17/2023] [Indexed: 07/06/2023] Open
Abstract
Moving a new drug from bench to bedside is a long and arduous process. The tactic of drug repurposing, which solves "new" diseases with "old" existing drugs, is more efficient and economical than conventional ab-initio way for drug development. Information technology has dramatically changed the paradigm of biomedical research in the new century, and drug repurposing studies have been significantly accelerated by implementing informatics techniques related to genomics, systems biology and biophysics during the past few years. A series of remarkable achievements in this field comes with the practical applications of in silico approaches including transcriptomic signature matching, gene-connection-based scanning, and simulated structure docking in repositioning drug therapies against breast cancer. In this review, we systematically curated these impressive accomplishments with summarization of the main findings on potentially repurposable drugs, and provide our insights into the current issues as well as future directions of the field. With the prospective improvement in reliability, the computer-assisted repurposing strategy will play a more critical role in drug research and development.
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Affiliation(s)
- Hui Zhou
- Department of Lymphoma and Hematology, The Affiliated Cancer Hospital of Xiangya School of Medicine, Central South University, Changsha, Hunan, People’s Republic of China
- Department of Lymphoma and Hematology, Hunan Cancer Hospital, Changsha, Hunan, People’s Republic of China
| | - Hongdou Liu
- Department of Laboratory Diagnosis, Changsha Kingmed Center for Clinical Laboratory, Changsha, Hunan, People’s Republic of China
| | - Yan Yu
- Department of Laboratory Diagnosis, Changsha Kingmed Center for Clinical Laboratory, Changsha, Hunan, People’s Republic of China
| | - Xiao Yuan
- Department of Laboratory Diagnosis, Changsha Kingmed Center for Clinical Laboratory, Changsha, Hunan, People’s Republic of China
- Department of Laboratory Diagnosis, Guangzhou Kingmed Center for Clinical Laboratory, Guangzhou, Guangdong, People’s Republic of China
| | - Ling Xiao
- Department of Histology and Embryology of Xiangya School of Medicine, Central South University, Changsha, Hunan, People’s Republic of China
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Kang H, Hou L, Gu Y, Lu X, Li J, Li Q. Drug-disease association prediction with literature based multi-feature fusion. Front Pharmacol 2023; 14:1205144. [PMID: 37284317 PMCID: PMC10239876 DOI: 10.3389/fphar.2023.1205144] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/13/2023] [Accepted: 05/09/2023] [Indexed: 06/08/2023] Open
Abstract
Introduction: Exploring the potential efficacy of a drug is a valid approach for drug development with shorter development times and lower costs. Recently, several computational drug repositioning methods have been introduced to learn multi-features for potential association prediction. However, fully leveraging the vast amount of information in the scientific literature to enhance drug-disease association prediction is a great challenge. Methods: We constructed a drug-disease association prediction method called Literature Based Multi-Feature Fusion (LBMFF), which effectively integrated known drugs, diseases, side effects and target associations from public databases as well as literature semantic features. Specifically, a pre-training and fine-tuning BERT model was introduced to extract literature semantic information for similarity assessment. Then, we revealed drug and disease embeddings from the constructed fusion similarity matrix by a graph convolutional network with an attention mechanism. Results: LBMFF achieved superior performance in drug-disease association prediction with an AUC value of 0.8818 and an AUPR value of 0.5916. Discussion: LBMFF achieved relative improvements of 31.67% and 16.09%, respectively, over the second-best results, compared to single feature methods and seven existing state-of-the-art prediction methods on the same test datasets. Meanwhile, case studies have verified that LBMFF can discover new associations to accelerate drug development. The proposed benchmark dataset and source code are available at: https://github.com/kang-hongyu/LBMFF.
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Affiliation(s)
- Hongyu Kang
- Department of Biomedical Engineering, School of Life Science, Beijing Institute of Technology, Beijing, China
- Institute of Medical Information, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Li Hou
- Institute of Medical Information, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Yaowen Gu
- Institute of Medical Information, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Xiao Lu
- Department of Biomedical Engineering, School of Life Science, Beijing Institute of Technology, Beijing, China
| | - Jiao Li
- Institute of Medical Information, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Qin Li
- Department of Biomedical Engineering, School of Life Science, Beijing Institute of Technology, Beijing, China
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Ahmed F, Samantasinghar A, Manzoor Soomro A, Kim S, Hyun Choi K. A systematic review of computational approaches to understand cancer biology for informed drug repurposing. J Biomed Inform 2023; 142:104373. [PMID: 37120047 DOI: 10.1016/j.jbi.2023.104373] [Citation(s) in RCA: 15] [Impact Index Per Article: 15.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/14/2023] [Revised: 03/25/2023] [Accepted: 04/23/2023] [Indexed: 05/01/2023]
Abstract
Cancer is the second leading cause of death globally, trailing only heart disease. In the United States alone, 1.9 million new cancer cases and 609,360 deaths were recorded for 2022. Unfortunately, the success rate for new cancer drug development remains less than 10%, making the disease particularly challenging. This low success rate is largely attributed to the complex and poorly understood nature of cancer etiology. Therefore, it is critical to find alternative approaches to understanding cancer biology and developing effective treatments. One such approach is drug repurposing, which offers a shorter drug development timeline and lower costs while increasing the likelihood of success. In this review, we provide a comprehensive analysis of computational approaches for understanding cancer biology, including systems biology, multi-omics, and pathway analysis. Additionally, we examine the use of these methods for drug repurposing in cancer, including the databases and tools that are used for cancer research. Finally, we present case studies of drug repurposing, discussing their limitations and offering recommendations for future research in this area.
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Affiliation(s)
- Faheem Ahmed
- Department of Mechatronics Engineering, Jeju National University, Republic of Korea
| | | | | | - Sejong Kim
- Department of Internal Medicine, Seoul National University Bundang Hospital, Seongnam, Korea; Department of Internal Medicine, Seoul National University College of Medicine, Seoul, Korea.
| | - Kyung Hyun Choi
- Department of Mechatronics Engineering, Jeju National University, Republic of Korea.
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Ünsal Ü, Cüvitoğlu A, Turhan K, Işık Z. NMSDR: Drug repurposing approach based on transcriptome data and network module similarity. Mol Inform 2023; 42:e2200077. [PMID: 36411244 DOI: 10.1002/minf.202200077] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/05/2022] [Revised: 09/19/2022] [Accepted: 11/21/2022] [Indexed: 11/23/2022]
Abstract
Computational drug repurposing aims to discover new treatment regimens by analyzing approved drugs on the market. This study proposes previously approved compounds that can change the expression profile of disease-causing proteins by developing a network theory-based drug repurposing approach. The novelty of the proposed approach is an exploration of module similarity between a disease-causing network and a compound-specific interaction network; thus, such an association leads to more realistic modeling of molecular cell responses at a system biology level. The overlap of the disease network and each compound-specific network is calculated based on a shortest-path similarity of networks by accounting for all protein pairs between networks. A higher similarity score indicates a significant potential of a compound. The approach was validated for breast and lung cancers. When all compounds are sorted by their normalized-similarity scores, 36 and 16 drugs are proposed as new candidates for breast and lung cancer treatment, respectively. A literature survey on candidate compounds revealed that some of our predictions have been clinically investigated in phase II/III trials for the treatment of two cancer types. As a summary, the proposed approach has provided promising initial results by modeling biochemical cell responses in a network-level data representation.
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Affiliation(s)
- Ülkü Ünsal
- Department of Biostatistics and Medical Informatics, Karadeniz Technical University, 61080, Trabzon, Türkiye.,Department of Health Management, Karadeniz Technical University, 61080, Trabzon, Türkiye
| | - Ali Cüvitoğlu
- Department of Computer Engineering, Dokuz Eylul University, 35390, İzmir, Türkiye
| | - Kemal Turhan
- Department of Biostatistics and Medical Informatics, Karadeniz Technical University, 61080, Trabzon, Türkiye
| | - Zerrin Işık
- Department of Computer Engineering, Dokuz Eylul University, 35390, İzmir, Türkiye
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Using Artificial Intelligence for Drug Discovery: A Bibliometric Study and Future Research Agenda. Pharmaceuticals (Basel) 2022; 15:ph15121492. [PMID: 36558943 PMCID: PMC9785219 DOI: 10.3390/ph15121492] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/31/2022] [Revised: 11/23/2022] [Accepted: 11/27/2022] [Indexed: 12/03/2022] Open
Abstract
Drug discovery is usually a rule-based process that is carefully carried out by pharmacists. However, a new trend is emerging in research and practice where artificial intelligence is being used for drug discovery to increase efficiency or to develop new drugs for previously untreatable diseases. Nevertheless, so far, no study takes a holistic view of AI-based drug discovery research. Given the importance and potential of AI for drug discovery, this lack of research is surprising. This study aimed to close this research gap by conducting a bibliometric analysis to identify all relevant studies and to analyze interrelationships among algorithms, institutions, countries, and funding sponsors. For this purpose, a sample of 3884 articles was examined bibliometrically, including studies from 1991 to 2022. We utilized various qualitative and quantitative methods, such as performance analysis, science mapping, and thematic analysis. Based on these findings, we furthermore developed a research agenda that aims to serve as a foundation for future researchers.
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